However, plenty of improvement remains for our method. We have shown our method generalizes to other sketch styles and different poses in Section REF , but our method is not guaranteed to work for all sketches. For example, we tested our method on horse sketches from Picasso and the Attneave's sleeping cat [1]}. As shown in Figure REF , our method cannot create a model that matches the pose faithfully. We note that Picasso's sketches are drawn with a distinctive style, and Attneave's cat depicts a complex pose, both of which are potential reasons for the failure.
Another limitation is that customizing a model in real-time is impossible with our current method, since our models take more than 30K iterations to train.
Our method requires access to the training set of the original model, which may make it inappropriate for settings where this data is not available. Finally, while our method enables flexible control of shape and poses, it cannot customize other properties such as color and texture. To expand expressiveness, we note our cross-domain loss can be applied to other inputs such
as VGG features, color scribbles, or semantic layouts.
